1. Field of the Invention
The present invention relates to a language processor using referring expressions and a probability calculating method used in language processing by the language processor.
2. Background Art
Assume that a robot communicates with a person using a speech dialogue system or the like and the person specifies a table by a referring expression “the white table with a red leg” in a room in which plural tables and chairs exist. Understanding of the referring expression means that a language processor of the robot identifies the table specified by the person. Generation of a referring expression means that the language processor of the robot generates a human-friendly expression which represents a table specified by the robot. Of course, referring expressions made by persons depend on knowledge of them and therefore the language processor of the robot is required to utilize information on the knowledge of persons in understanding and generation of referring expressions.
Generally, a language processor can use a probabilistic model to utilize information on knowledge of persons in understanding and generation of referring expressions.
Conventionally, it has been proposed to introduce a probabilistic model to overcome uncertainties due to discrepancies in knowledge and cognition between subjects (Horacek, H.: Generating referential descriptions under conditions of uncertainty, in Proc. The 10th European Workshop on Natural Language Generation (ENLG) (2005)).
However, the probabilistic model described above is not designed in sufficient consideration of application to a real environment such as a communication between a person and a robot. In a real environment referring expressions representing objects with a complicated structure such as “the white table with a red leg” and “the white table with red corners” have to be handled. However, the probabilistic model described above is not designed in sufficient consideration of handling referring expressions representing objects with a complicated structure. Further, the probabilistic model described above cannot be used for generation of referring expressions.
Thus, a language processor using a probabilistic model which can handle referring expressions representing objects with a complicated structure and can be used both for understanding and generation of referring expressions has not been developed.
Accordingly there is a need for a language processor using a probabilistic model which can handle referring expressions representing objects with a complicated structure and can be used both for understanding and generation of referring expressions.